Pomo Raises $4.5M to Bring Agent Intelligence to Marketing Decisions
Pomo, an agentic marketing intelligence platform, raised $4.5 million in seed funding led by Kindred Ventures, with participation from Databricks Ventures, SV Angel, 645 Ventures, and angel investors including Scott Belsky (Adobe) and Mehdi Ghissassi (Google DeepMind).
The founding team is strong. CEO Dutta led applied generative AI and reinforcement learning at Google DeepMind, working on Imagen and Gemini models for advertising, climate, and recommender systems. CTO Joe Cheuk was a Staff Engineer at Databricks, Meta, and Google Cloud. They saw how fragmented tools and siloed data slow marketing decisions and built Pomo to apply agent principles to the problem.
Pomo targets mid-market companies specifically, the segment too big for manual marketing analysis but too small for enterprise data science teams. The platform applies real-time intelligence to marketing decisions rather than just generating content, which is what most AI marketing tools actually do. The distinction matters: content generation is commoditized, but decision support for budget allocation, channel mix, and campaign optimization is where the real value sits.
The company plans to use the funding to expand its engineering and applied AI teams. With a DeepMind RL background applied to marketing decision loops, Pomo is one of the more technically grounded seed-stage plays in the agentic marketing space.
https://finance.yahoo.com/sectors/technology/articles/ex-google-deepmind-databricks-engineers-110000712.html
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The founding team is strong. CEO Dutta led applied generative AI and reinforcement learning at Google DeepMind, working on Imagen and Gemini models for advertising, climate, and recommender systems. CTO Joe Cheuk was a Staff Engineer at Databricks, Meta, and Google Cloud. They saw how fragmented tools and siloed data slow marketing decisions and built Pomo to apply agent principles to the problem.
Pomo targets mid-market companies specifically, the segment too big for manual marketing analysis but too small for enterprise data science teams. The platform applies real-time intelligence to marketing decisions rather than just generating content, which is what most AI marketing tools actually do. The distinction matters: content generation is commoditized, but decision support for budget allocation, channel mix, and campaign optimization is where the real value sits.
The company plans to use the funding to expand its engineering and applied AI teams. With a DeepMind RL background applied to marketing decision loops, Pomo is one of the more technically grounded seed-stage plays in the agentic marketing space.
https://finance.yahoo.com/sectors/technology/articles/ex-google-deepmind-databricks-engineers-110000712.html
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